{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8bc1a0e9-58df-4b51-b055-1ab71ca49cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba919e2-0606-4672-aa9f-344fc7138d35",
   "metadata": {},
   "source": [
    "# 在原始空间中求解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5128068c-2570-42b2-b9b5-8345e8c9641e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf65785-13f0-44ed-ac0d-d4d3391f5b20",
   "metadata": {},
   "source": [
    "### 设置训练集和测试集数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "283d869f-9975-4056-89e6-716b810f6ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 2 3\n",
    "target = 2\n",
    "# 对偶空间或是原空间\n",
    "is_dual = True\n",
    "\n",
    "if target==1:\n",
    "    data_name = '0618'\n",
    "elif target==2:\n",
    "    data_name = '0854'\n",
    "elif target==3:\n",
    "    data_name = '1066'\n",
    "\n",
    "# 设置训练集数据\n",
    "# 获取指定的CSV文件\n",
    "csv_files = glob.glob(f'./RGB_data/data_{data_name}_new.csv')\n",
    "\n",
    "# 设置测试集数据\n",
    "new_image_path = f'./input_data/{data_name}.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d2b754-0265-4117-a546-538da2f31e9b",
   "metadata": {},
   "source": [
    "# TRAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac41cd2a-913c-4322-aa1b-f187c36f371e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43, 4)\n"
     ]
    }
   ],
   "source": [
    "# 初始化一个空的DataFrame来存储合并后的数据\n",
    "data = pd.DataFrame()\n",
    "\n",
    "# 遍历所有CSV文件并将它们合并\n",
    "for file in csv_files:\n",
    "    df = pd.read_csv(file)\n",
    "    data = pd.concat([data, df], ignore_index=True)\n",
    "\n",
    "# 现在all_data包含了所有CSV文件的数据\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c842420-8c91-4fa3-bfa2-1f43a100dabd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((43, 3), (43,), pandas.core.frame.DataFrame, pandas.core.series.Series)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入特征和标签\n",
    "features = data.drop('Label', axis=1) # 按列操作取名为’Label以外的列\n",
    "labels = data['Label']\n",
    "features.shape,labels.shape,type(features),type(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bad1adc-8161-43ca-a09d-8f7b5cb4d764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43, 3)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(43, 3)\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(pandas.core.frame.DataFrame, pandas.core.series.Series)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 归一化\n",
    "scaler = StandardScaler()\n",
    "\n",
    "print(features.shape)\n",
    "print(type(features))\n",
    "\n",
    "features = scaler.fit_transform(features)\n",
    "features = pd.DataFrame(features)\n",
    "\n",
    "print(features.shape)\n",
    "print(type(features))\n",
    "\n",
    "features.shape,labels.shape,\n",
    "type(features),type(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "48be8177-c8cf-4f42-bbe5-72cd3071ab0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels,\n",
    "                                     test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4468918-0113-40b8-b69f-57362fd359c3",
   "metadata": {},
   "source": [
    "## 线性SVM 原始空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5394eb7d-e419-4fd0-8b57-def5227fb63a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 10}\n",
      "Best cross-validation score: 0.8583333333333332\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#搜索C\n",
    "param_grid = {\n",
    "    'C': [1e-4, 1e-3, 1e-2, 0.1, 1, 10, 100]\n",
    "}\n",
    "\n",
    "#创建svm分类器\n",
    "#dual参数决定了是否求解对偶问题,True表示算法将在对偶空间中求解False表示算法将在原空间中求解\n",
    "clf = svm.LinearSVC(dual=is_dual, random_state=42)\n",
    "#通过GridSearch设置参数搜索和k折交叉验证\n",
    "grid_search = GridSearchCV(estimator=clf, param_grid=param_grid\n",
    "                           , cv=10, scoring='accuracy')\n",
    "#模型训练\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 输出最佳参数和最佳分数\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best cross-validation score:\", grid_search.best_score_)\n",
    "\n",
    "# 使用最佳参数的模型在测试集(实际上是验证集)上进行预测\n",
    "clf = grid_search.best_estimator_\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a8c410a-705f-40af-8443-9d165be3c383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "共43个数据, 27个正样本，16个负样本\n",
      "\n",
      "Model accuracy: 1.00\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         4\n",
      "           1       1.00      1.00      1.00         7\n",
      "\n",
      "    accuracy                           1.00        11\n",
      "   macro avg       1.00      1.00      1.00        11\n",
      "weighted avg       1.00      1.00      1.00        11\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#输出训练结果\n",
    "# 数据量\n",
    "positive_count = data[data['Label'] == 1]['Label'].count()\n",
    "negative_count = data[data['Label'] == 0]['Label'].count()\n",
    "\n",
    "print(f'共{data.shape[0]}个数据, {positive_count}个正样本，{negative_count}个负样本\\n')\n",
    "\n",
    "# 计算并打印准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Model accuracy: {accuracy:.2f}\\n')\n",
    "\n",
    "# 计算并打印分类报告\n",
    "report = classification_report(y_test, y_pred)\n",
    "print('Classification Report:\\n', report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31ca4087-55e9-4956-bfd4-14e5617ee1a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n",
    "\n",
    "##假设 clf 是训练好的 SVM 模型\n",
    "##clf = SVC().fit(X_train, y_train)\n",
    "\n",
    "# 保存模型\n",
    "dump(clf, './model/svm_model_origin.joblib')\n",
    "\n",
    "# 加载模型\n",
    "clf_loaded = load('./model/svm_model_origin.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5815d8fd-2c09-4c61-8063-07ba9f8ec997",
   "metadata": {},
   "source": [
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e893c72-470d-467e-a51d-19b9d31151ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "from joblib import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a1bf4d3-9411-495c-8c14-b84805b95fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(model_path):\n",
    "    \"\"\"加载训练好的SVM模型\"\"\"\n",
    "    return load(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9658fa3c-fff7-4d4f-864f-ca7b95ff9c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image):\n",
    "    h, w, _ = image.shape\n",
    "    \n",
    "    blue_channel = image[:, :, 0].reshape(-1)\n",
    "    green_channel = image[:, :, 1].reshape(-1)\n",
    "    red_channel = image[:, :, 2].reshape(-1)\n",
    "\n",
    "    img_array = np.stack((blue_channel, green_channel, red_channel), axis=1)\n",
    "\n",
    "    print(img_array.shape)\n",
    "    \n",
    "    return img_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4da2fb2-4cd8-41b4-92dd-8edd8a99df92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((250, 500, 3), numpy.ndarray)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 假设你已经有了一个训练好的模型 clf 和一个 StandardScaler 对象 scaler\n",
    "model_path = './model/svm_model_origin.joblib'\n",
    "clf = load_model(model_path)\n",
    "\n",
    "# 加载新的图片\n",
    "new_image = cv2.imread(new_image_path)\n",
    "new_image.shape, type(new_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ad350af-aeea-4f98-9c7a-ec25d50bfb83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125000, 3)\n"
     ]
    }
   ],
   "source": [
    "# 将图片数据转换为模型可以处理的格式\n",
    "new_features = preprocess_image(new_image) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51d0b2da-7d30-49ec-9278-b1542ad027d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/crisi/miniconda3/envs/PR/lib/python3.9/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((125000, 3), numpy.ndarray)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用相同的StandardScaler进行标准化\n",
    "new_features = scaler.transform(new_features)\n",
    "new_features.shape, type(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43cde813-fa18-4def-8874-85d46abe880f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用模型进行预测\n",
    "prediction = clf.predict(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c21bf4da-84fd-4e35-8df7-3bed81d7c0a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用模型进行预测\n",
    "#new_features 已经是归一化后的数组，并且形状正确\n",
    "predictions = clf.predict(new_features)\n",
    "\n",
    "#将预测结果应用到图片上\n",
    "#original_image 是原始图片的NumPy数组\n",
    "original_image = new_image\n",
    "height, width, _ = original_image.shape\n",
    "predicted_image = np.zeros_like(original_image)\n",
    "\n",
    "for i in range(height):\n",
    "    for j in range(width):\n",
    "        #获取当前像素的索引\n",
    "        pixel_index = i * width + j\n",
    "        \n",
    "        #根据预测结果设置像素的颜色\n",
    "        if predictions[pixel_index] == 1:\n",
    "            predicted_image[i, j] = (255, 255, 255)  # 白色\n",
    "        else:\n",
    "            predicted_image[i, j] = (0, 0, 0)  # 黑色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12de4fb7-4df6-4c8b-b408-2273e821ba5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted image saved to ./result_new/pred_0854_dual_.jpg\n"
     ]
    }
   ],
   "source": [
    "# 显示原始图片\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))\n",
    "plt.title('Original Image')\n",
    "plt.axis('off')\n",
    "\n",
    "# 显示预测后的图片\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(cv2.cvtColor(predicted_image, cv2.COLOR_BGR2RGB))\n",
    "plt.title('Predicted Image')\n",
    "plt.axis('off')\n",
    "\n",
    "# 显示图像\n",
    "plt.show()\n",
    "    \n",
    "# 保存预测后的图片\n",
    "new_image_name = os.path.splitext(os.path.basename(new_image_path))[0]\n",
    "\n",
    "if is_dual == False:\n",
    "    save_is_dual = 'origin'\n",
    "elif is_dual == True:\n",
    "    save_is_dual = 'dual'\n",
    "\n",
    "save_path = f'./result_new/pred_{new_image_name}_{save_is_dual}_.jpg'\n",
    "cv2.imwrite(save_path, predicted_image)\n",
    "print(f'Predicted image saved to {save_path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e90dc151-1d7a-4859-bcf0-cf0c92080018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 1.656383752822876 seconds\n"
     ]
    }
   ],
   "source": [
    "end_time = time.time()\n",
    "# 计算并打印运行时间\n",
    "elapsed_time = end_time - start_time\n",
    "print(f\"Elapsed time: {elapsed_time} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55f2983d-b9a0-484f-9816-22052e3a12e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel Accuracy (PA): 0.6777\n",
      "Intersection over Union (IoU): 0.4253\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def calculate_performance_metrics(y_true, y_pred):\n",
    "    # 计算混淆矩阵\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "    \n",
    "    # 计算像素准确率（PA）\n",
    "    pa = (tp + tn) / (tp + tn + fp + fn)\n",
    "    \n",
    "    # 计算交并比（IoU）\n",
    "    iou = tp / (tp + fp + fn)\n",
    "    \n",
    "    return pa, iou\n",
    "\n",
    "def load_and_mask_binary_image(image_path):\n",
    "    # 加载图像\n",
    "    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "    if image is None:\n",
    "        raise ValueError(f\"无法加载图像：{image_path}\")\n",
    "    \n",
    "    # 创建掩码以仅保留二值像素（0或255）\n",
    "    mask = (image == 0) | (image == 255)\n",
    "    # 应用掩码并转换为二值图像\n",
    "    binary_image = image.copy()\n",
    "    binary_image[~mask] = 0  # 将非二值像素设置为0\n",
    "    return binary_image, mask\n",
    "\n",
    "# 输入图像的路径\n",
    "test_image_path = save_path  # 测试得到的图像路径\n",
    "annotated_image_path = f'./input_data/{data_name}_label.png'  # 标注的图像路径\n",
    "\n",
    "# 加载并处理图像\n",
    "y_true, true_mask = load_and_mask_binary_image(annotated_image_path)\n",
    "y_pred, _ = load_and_mask_binary_image(test_image_path)\n",
    "\n",
    "# 将图像转换为二值数组（0或1）\n",
    "y_true_binary = (y_true > 0).astype(int)\n",
    "y_pred_binary = (y_pred > 0).astype(int)\n",
    "\n",
    "# 应用掩码以仅考虑二值像素\n",
    "y_true_binary = y_true_binary[true_mask]\n",
    "y_pred_binary = y_pred_binary[true_mask]\n",
    "\n",
    "# 计算性能指标\n",
    "pa, iou = calculate_performance_metrics(y_true_binary, y_pred_binary)\n",
    "\n",
    "print(f\"Pixel Accuracy (PA): {pa:.4f}\")\n",
    "print(f\"Intersection over Union (IoU): {iou:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dea01f3-596b-401d-80d3-74a41f71195d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
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